Please use this identifier to cite or link to this item: https://doi.org/10.1145/2331140.2331145
Title: Bayesian kriging analysis and design for stochastic simulations
Authors: Ng, S.H. 
Yin, J.
Keywords: Bayesian statistics
Design of experiments
Kriging
Parameter uncertainty
Stochastic simulation
Issue Date: Aug-2012
Source: Ng, S.H., Yin, J. (2012-08). Bayesian kriging analysis and design for stochastic simulations. ACM Transactions on Modeling and Computer Simulation 22 (3) : -. ScholarBank@NUS Repository. https://doi.org/10.1145/2331140.2331145
Abstract: Kriging is an increasingly popular metamodeling tool in simulation due to its flexibility in global fitting and prediction. In the fitting of this metamodel, the parameters are often estimated from the simulation data, which introduces parameter estimation uncertainties into the overall prediction error. Traditional plug-in estimators usually ignore these uncertainties, which can be substantial in stochastic simulations. This typically leads to an underestimation of the total variability and an overconfidence in the results. In this article, a Bayesian metamodeling approach for kriging prediction is proposed for stochastic simulations to more appropriately account for the parameter uncertainties. We derive the predictive distribution under certain assumptions and also provide a general Markov Chain Monte Carlo analysis approach to handle more general assumptions on the parameters and design. Numerical results indicate that the Bayesian approach has better coverage and better predictive variance than a previously proposed modified nugget effect kriging model, especially in cases where the stochastic variability is high. In addition, we further consider the important problem of planning the experimental design. We propose a two-stage design approach that systematically balances the allocation of computing resources to new design points and replication numbers in order to reduce the uncertainties and improve the accuracy of the predictions. © 2012 ACM.
Source Title: ACM Transactions on Modeling and Computer Simulation
URI: http://scholarbank.nus.edu.sg/handle/10635/63042
ISSN: 10493301
DOI: 10.1145/2331140.2331145
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

11
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

7
checked on Nov 21, 2017

Page view(s)

30
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.